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<p>Sugarcane (<i>Saccharum officinarum</i> L.) is one of the largest crops in Brazil, and its productivity varies according to the environment and management practices adopted. In this study, tons of sugar per hectare (TSH) are estimated using a heteroscedastic gamma (GA) regression model, which considers several explanatory variables, one of which is the normalized difference green vegetation index (GNDVI), obtained from multispectral images in two locations over two consecutive growing seasons. The modeling considers regression structures in the parameters representing the mean and coefficient of variation, respectively. The results show that there is an influence of location, cultivar, cycle, accumulated precipitation, and GNDVI. To verify if the model is well-fitted to the data, the analysis of quantile residuals shows that the model is adequate. Therefore, the results indicate that heteroscedastic GA regression is an alternative model for predicting TSH and can assist in decision-making in sugarcane cultivation.</p>

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Estimating Sugar Yield in Sugarcane Using Green Normalized Difference Vegetation Index Derived from Imagery Obtained by Remotely Piloted Aircrafts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月3日

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  • Julio Cezar Souza Vasconcelos,
  • Caio Simplicio Arantes,
  • Eduardo Antonio Speranza,
  • João Francisco Gonçalves Antunes,
  • Luiz Antonio Falaguasta Barbosa,
  • Geraldo Magela de Almeida Cançado

摘要

Sugarcane (Saccharum officinarum L.) is one of the largest crops in Brazil, and its productivity varies according to the environment and management practices adopted. In this study, tons of sugar per hectare (TSH) are estimated using a heteroscedastic gamma (GA) regression model, which considers several explanatory variables, one of which is the normalized difference green vegetation index (GNDVI), obtained from multispectral images in two locations over two consecutive growing seasons. The modeling considers regression structures in the parameters representing the mean and coefficient of variation, respectively. The results show that there is an influence of location, cultivar, cycle, accumulated precipitation, and GNDVI. To verify if the model is well-fitted to the data, the analysis of quantile residuals shows that the model is adequate. Therefore, the results indicate that heteroscedastic GA regression is an alternative model for predicting TSH and can assist in decision-making in sugarcane cultivation.